An integrated model of concept learning and word-concept mapping


To learn the meaning of a new word, children must solve two distinct problems: identify the referent under ambiguity and determine how to generalize that word’s meaning to new objects. Traditionally, these two problems have been addressed separately in the literature, despite their tight relationship with one another. We present a hierarchical Bayesian model that jointly infers both the referent of a word in ambiguous con- texts and the concept associated with a word. As a first step in testing this model, we provide evidence that our model fits human data in a simple cross-situational concept learning task.

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